-Multi-agent systems (MASs) have become an important topic in distributed systems research. These distributed multi-agent systems call for special software modeling methods that explicitly support key system properties such as resource constraints and control of conflicts. Although there have been some system modeling techniques to support MASs design and automatic analysis, most state-of-the-art techniques have not distinguished potential conflicts from real conflicts during the design stage. To solve this problem, we define a new concept, called "potential arcs," which is integrated into colored Petri net modeling to support the modeling of MASs. We present a modeling methodology based on the potential arc concept and illustrate the methodology with a case study, including some associated model analysis.

-One approach to modeling multi-agent systems (MAS) is to employ a method that defines components which describe the local behavior of individual agents, as well as a special component, called a coordinator. The coordinator component coordinates the resource-sharing behavior among the agents. The agent models define a set of local plans, and the combination of local plans and a coordinator defines a system's global plan. Although earlier work has provided the base functionality needed to synthesize inter-agent resource sharing behavior for a global, conflict-free MAS environment, the lack of coordination flexibility limits the modeling capability at both the local plan level and the global plan level. In this paper, we describe a flexible design method that supports a range of coordinator components. The method defines four levels of coordination and an associated four-step coordinator generation process, which allows for the design of coordinators with increasing capabilities for handling complexity associated with resource coordination. Colored Petri Net-based simulation is used to analyze various properties that derive from different coordinators and synthesis of a reduced coordinator component is discussed for cases that involve homogeneous agents.

We present a comprehensive unified modeling language (UML) statechart diagram analysis framework. This framework allows one to progressively perform different analysis operations to analyze UML statechart diagrams at different levels of model complexity. The analysis operations supported by the framework are based on analyzing Petri net models converted from UML statechart diagrams using a previously proposed transformation approach. After introducing the general framework, the paper emphasizes two simulation-based analysis operations from the framework: direct MSC inspection, which provides a visual representation of system behavior described by statechart diagrams; and a pattern-based trace query technique, which can be used to define and query system properties. Two case-study examples are presented with different emphasis. The gas station example is a simple multi-object system used to demonstrate both the visual and query-based analysis operations. The early warning system example uses only one object, but features composite states and includes analysis specifically aimed at one composite state feature, history states.

Automated testing typically uses specifications to drive the generation of test inputs and/or the checking of program outputs. Many software systems have structurally complex inputs that cannot be adequately described using simple formalisms such as context-free grammars. In order to generate such inputs, many automated testing environments require the user to express the structure of the input using an unfamiliar formal notation. This raises the cost of employing automated testing, thereby offsetting the benefits gained. We present yagg (yet another generator-generator), a tool that allows the programmer to specify the input using a syntax very similar to that of LEX and YACC, widely used scanner and parser generators. yagg allows the user to bound the input space using several different techniques, and generates an input generator that systematically enumerates inputs. We evaluate the ease of use and performance of the tool relative to a model checker-based generator used in previous research. Our experiences indicate that yagg generators can be somewhat slower, but that the ease-of-use afforded by the familiar syntax may be attractive to users.

This article presents a new learning method based on machine learning, which can quickly and accurately draw up the characterization of the Static Random-Access Memory (SRAM) compiler and standard cell library. The timing of 10 standard circuits with different process corners and their key parameters were collected. According to the 10-fold cross validation, the regression model was established by linear regression. After comparing the influence of different parameters on path delay, determination coefficient of training set and testing set were 0.979 and 0.955, respectively. Relative Error of training set and test set were 0.9458 and 0.8736, respectively. Then, the timing of a D type flip-flop (DFF) was selected as the target of the regression. At the same time, the determination coefficients of the regression model training set and the testing set are 0.9992 and 0.9992, respectively. Relative Error of training set and test set were 0.9882 and 0.9868, respectively. The results show that the model fitted the timing of DFF by the path delay of other circuits is better than the previous method.